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The Quality Effect: Does Financial Liberalization Improve the Allocation of Capital?

Abdul Abiad, Nienke Oomes, and Kenichi Ueda*

December 2004

Abstract

This study documents evidence of a "quality effect" of financial liberalization on allocative efficiency, as measured by dispersion in Tobin's Q across firms. Based on a simple model, we predict that financial liberalization, by equalizing access to credit, reduces the variation in expected marginal returns. We test this prediction using a new financial liberalization index and firm-level data for five emerging markets: India, Jordan, Korea, Malaysia, and Thailand.

We find strong evidence that financial liberalization, rather than financial deepening, improves allocative efficiency.

JEL Classification Numbers:D61, E44, G14, G18, O16

Keywords:Tobin's Q, financial liberalization, investment, allocative efficiency

Author’s E-Mail Address:[email protected]; [email protected]; [email protected]

______International Monetary Fund 700 19th Street, N.W., Washington, D.C. The corresponding author is Kenichi Ueda, (202)623-6368, [email protected]. The views expressed in this paper are those of the authors and do not necessarily represent those of the IMF or IMF policy. We would like to thank Xavier Gine, Aart Kraay, Inessa Love, Ashoka Mody, Robert Townsend, and other participants at IMF and World Bank seminars for helpful comments and suggestions; and Tom Walter for excellent editorial comments. The usual caveats apply. - 2 -

I. INTRODUCTION

The rationale for financial liberalization has been based on two potential benefits: a quantity effect, manifested in higher levels of savings and investment in an economy, and a quality effect, manifested in a more efficient allocation of capital. While a growing body of literature has found little evidence of a quantity effect, this paper takes a different approach and reports evidence of a quality effect.

Previous research on this subject has been constrained by the problem of how to measure efficiency in allocating capital. One of the main contributions of this paper is that it proposes a new measure of allocative efficiency, based on the variation in expected returns to investment across firms. We show, in a simple model, that if liberalization is truly efficiency enhancing, this variation should be lower in countries with more liberalized financial sectors, where markets determine the allocation of credit, and should be higher in countries with less liberalized financial sectors, where the allocation of credit is determined by governments. Intuitively, the removal of government controls implies that credit will be reallocated from firms with low expected returns to investment in firms with high expected returns, thereby raising expected returns for the former and reducing them for the latter. We measure the variation in expected returns by the dispersion in Tobin’s Q, which measures the market value of a firm’s securities relative to the replacement cost of its assets, while controlling for industry and age effects. We calculate this dispersion for five emerging-market economies—India, Jordan, Korea, Malaysia, and Thailand—from 1980 to 1994, in total covering 413 firms.

Another important contribution of this paper is the distinction that it makes between financial liberalization and financial deepening—a distinction not often made in the literature. Financial - 3 - liberalization, on the one hand, refers to a reduction in the role of government, and an increase in the role of the market, in allocating credit. We measure this using a new financial liberalization index, which takes into account credit controls, interest rate controls, entry barriers for banks, regulations, privatization, and restrictions on international financial transactions. Financial deepening, on the other hand, refers to an increase in the volume of credit being intermediated in financial markets and is typically measured by indicators such as M2, credit to the private sector, or stock market capitalization relative to GDP.1 Although the two will tend to be related, they are not equivalent.2 Financial deepening affects access to finance, while liberalization affects the incentives with which credit is deployed. We investigate the role each factor plays in allocative efficiency.

Our main result is that financial liberalization improves the allocation of capital, thus providing an affirmative answer to the question posed in the title of this paper. We present two types of evidence in favor of this claim. First, we present descriptive statistics that show that the dispersion in Tobin’s Q decreased in all of the five countries in our sample following financial liberalization. Second, we run several panel regressions of dispersion in Q on financial liberalization and show that the coefficient on financial liberalization is strongly negative, even after controlling for omitted variables, time trends, endogeneity, and other factors.

1 It is unfortunate that many recent studies have used the term “financial development” when referring specifically to financial deepening. Financial development should be thought of as a much broader concept reflecting improvements in the functioning of the financial sector. These include increased access to financial intermediation, greater diversification opportunities, improved information quality, and better incentives for prudent lending and monitoring.

2 For example, during the 1970s and up into the early 1980s, Japan and France had financially deep markets that were highly repressed. Conversely, the United Kingdom in the late 1970s and several Latin American countries, including Peru, Argentina, and Brazil, in the 1990s had liberalized financial markets that were relatively shallow. - 4 -

We have three additional findings. First, financial liberalization appears to be more important than financial deepening for improving allocative efficiency; in fact, once financial liberalization is controlled for, increased private credit in our sample is associated with lower allocative efficiency. This result holds even when controlling for stock market liquidity (as measured by stock market turnover relative to market capitalization) to eliminate effects of potential misallocations of credit during lending booms before financial liberalization. Second, stock market liquidity itself lowers the dispersion of marginal returns. This reflects an increase in the accuracy of stock market valuation as stock markets become more liquid. Third, we observe in several countries that the dispersion in Q seems to have increased gradually prior to liberalization. This suggests that credit allocation in these countries worsened over time, as credit was continually directed toward firms that were already above their optimal sizes, and was deprived of those that needed it most.

The paper is organized as follows. Section II reviews the literature on the quantity effects and quality effects of financial liberalization, as well as the related literature on financial deepening.

Section III then presents a simple model that shows how government intervention in credit allocation generates variation in the rates of return to investment for firms that are otherwise identical in their production function, adjustment cost function, and real wage costs. Section IV explains our rationale for using the dispersion in Q as our measure of variation in expected marginal returns. Data sources and details on our new financial liberalization index are discussed in Section V. The descriptive statistics and regression results are presented in Sections VI and

VII, respectively. Section VIII concludes by summarizing our findings. - 5 -

II. RELATED LITERATURE

A. Studies on Quantity Effects

Much of the literature on financial liberalization has focused on the question of whether it has a positive “quantity effect,” as manifested in higher levels of savings and investment. One theoretical argument in favor of such a quantity effect, going back to McKinnon (1973) and

Shaw (1973), is that higher interest rates that follow the removal of interest rate ceilings will generate higher savings, and in turn, higher investment. Higher rates of return may also result from better insurance against future risk, which, as Obstfeld (1994) argues may induce a shift toward higher-risk, higher-return projects. Finally, a positive quantity effect on investment may be expected because increased competition between banks can lead firms to internalize external effects in investment (Ueda, 2000).

However, there are also reasons to expect a negative, or at least ambiguous, quantity effect of financial liberalization. First, even if rates of return increase with improved risk sharing or following the removal of interest rate ceilings, the quantity effect on savings and investment will depend on whether income or substitution effects prevail. If income effects prevail, the quantity effect would be negative, while if substitution effects prevail, the quantity effect would be positive. Second, if liberalized financial sectors provide for better insurance against future risk, this may lower the incentive to save for future needs (Devereux and Smith, 1994).3 Thus, financial liberalization can improve the functioning of the financial sector without necessarily increasing savings and investment.

3 Without a change in international financing constraints, these lower savings would also need to be accompanied by lower investment. - 6 -

Since the direction of the quantity effect is theoretically ambiguous, it is perhaps not surprising that empirical studies find mixed, and occasionally even negative, effects of liberalization on savings and investment. Using both a cointegration and an augmented Euler equation approach,

Bandiera and others (2000) show that, in a sample of eight developing countries, financial liberalization is not associated with an increase in savings. In fact, certain aspects of liberalization—those that reduce liquidity constraints for household consumption—are associated with a fall in savings. Several country studies corroborate these findings. For example,

Jayaratne and Strahan (1996) find that the deregulation of bank branches in the United States in the 1970s did not increase the volume of bank lending, and Sancak (2002) finds that the 1980 financial reforms in Turkey did not lead to a reduction in financing constraints.

B. Studies on Quality Effects

Given the theoretically ambiguous direction of the quantity effect, we cannot evaluate the success of liberalization by assessing whether it has a positive or negative effect on savings and investment. Instead, we propose to judge the merits of liberalization by its “quality effect,” that is, by assessing whether it improves the allocation of capital across firms.

Thus far, only a few studies have attempted to estimate quality effects, and they have generally found positive results. In an early study, Cho (1988) finds that financial liberalization in Korea led to a decrease in the variation in borrowing costs, which he interprets as an improvement in allocative efficiency. In a more recent study, Galindo, Schiantarelli, and Weiss (2002) also report a positive and significant effect of liberalization on a measure of allocative efficiency, using firm-level data for 12 developing countries. Finally, in a somewhat different context, Chari and

Henry (2003) find that capital account liberalization improves the allocation of capital across countries, just as financial liberalization improves the allocation of capital within countries. - 7 -

In our view, the main problem with existing studies on quality effects has been their definition of allocative efficiency. For example, Cho (1988)’s definition of allocative efficiency as a reduction in the variation in borrowing costs is almost tautological, as this variation naturally decreases when governments eliminate directed credit and interest rate controls. Moreover, even if all firms faced identical borrowing costs, the allocation of capital would still not be efficient if access to credit were determined by noneconomic factors. Galindo, Schiantarelli, and Weiss (2002) use a more sophisticated definition and argue that, if capital is allocated more efficiently after financial liberalization, more capital should flow to firms with a higher marginal productivity of capital.

They test this hypothesis by assessing whether an investment-weighted average of ex post marginal returns increases relative to a naïve size-weighted average of ex post marginal returns.4

However, a problem with this definition is that, as they themselves note, “the marginal product of capital of a perfectly efficient economy would be the same in all firms. Consequently, random allocations of capital would do as well as any other allocation” (p. 12). In other words, in a perfectly efficient economy, the investment-weighted average should be equal to the size- weighted average. But this implies that the ratio of investment-weighted to size-weighted average ex post marginal returns should converge to one, not diverge away from one, which is the case for several countries in their sample. Moreover, while it is correct that ex ante

(expected) marginal returns should be equal across firms in equilibrium, the effect of financial liberalization on the ex post (realized) marginal returns is uncertain. On the one hand, the equalization of ex ante returns may indeed lead to an equalization of ex post returns. On the other hand, as Obstfeld (1994) suggests, the improved availability of risk insurance may lead firms to adopt higher-risk, higher-return projects, thus creating a larger dispersion of ex post returns.

4 They proxy ex post marginal returns by the sales-to-capital ratio and the operating-profits-to- capital ratio, under the assumption of a constant-returns-to-scale technology. - 8 -

C. Studies on Financial Deepening

Although this paper focuses on financial liberalization, our study is obviously closely related to the literature on financial deepening, in the context of which quantity and quality effects can also be discussed. The seminal papers in this literature are those by King and Levine (1993) and

Levine, Loayza, and Beck (2000), whose regression results suggest a positive effect of financial deepening (measured by M2 and the ratio of private credit to GDP) on subsequent economic growth. Recent studies have also used alternative approaches to study the effects of financial deepening. In an important cross-country study, Rajan and Zingales (1998) find that, in countries with less developed financial markets (whether measured by financial depth or improved accounting standards), industries that are more dependent on external finance grow more slowly than other industries. In another well-known study, Wurgler (2000) finds that in countries with deeper financial sectors, capital is better allocated in the sense that it tends to flow to growing industries.5 At the aggregate level, Beck, Levine, and Loayza (2000) find that financial deepening promotes GDP growth through increases in efficiency (i.e., total factor productivity), rather than through factor accumulation. Finally, Love (2001) finds that financial deepening is associated with a drop in the sensitivity of investment to the availability of internal funds, which can be interpreted as a proxy for financing constraints.

But is it really financial deepening that has led to the positive outcomes noted in these studies?

Or might it be financial liberalization—which is distinct from, but correlated with, financial depth—that matters? None of these studies controlled for financial liberalization, mainly due to

5 In our view, this is not necessarily evidence of a quality effect, since governments can artificially stimulate growth in certain industries using directed credit and differential interest rates. For example, while Japan is one of the most important examples in Wurgler’s study, the Japanese government is known to have substantially intervened in allocating capital among firms (see, e.g., Ito and others , 1988). - 9 - the absence of an adequate measure.6 In recent years, however, time-varying, cross-country measures of financial liberalization have been developed by Abiad and Mody (2003), Kaminsky and Schmukler (2003), and Edison and Warnock (2003). Given the importance of this distinction for policy prescriptions, we explicitly control for both financial liberalization and financial deepening in our panel regressions. Our results suggest that it is financial liberalization, rather than financial deepening, that improves allocative efficiency.

III. THE MODEL

In this section we present a simple model to show that the distribution of marginal returns to capital across firms becomes more equal (i.e., the allocation of capital becomes more efficient) as financial sectors become more liberalized.

We incorporate the classical Marshallian view that each firm, depending on the industry to which it belongs, has an optimal operating size. We thus write the profit function for a firm at time t as follows:

p(Kt , L t )= f ( K t , L t ) - wL t - f ( I t ) - RK t , (1) with a standard law of motion for capital:

Kt=(1 -d ) K t-1 + I t , (2) where K denotes capital, L denotes labor, w is the real market wage, I is investment, and R is the gross interest rate. The function f is a constant-returns-to-scale (CRS) production function with

6 Several authors (e.g., Greenwood and Jovanovic, 1990; and Acemoglue and Zilibotti, 1997) have argued that the comovement of financial deepening and economic growth is a transitional phenomenon, driven by decisions of economic agents. In that case, standard econometric techniques might not be sufficient to disentangle the effect of financial deepening on economic growth or related variables (Townsend and Ueda, 2003). However, our study is free from this criticism, because financial liberalization is an exogenous policy change to economic agents, and efficient capital allocation can be achieved at any level of economic growth. - 10 -

partial derivatives f1>0, f2>0, f11<0, f22<0, and f12>0. The function (It) measures the adjustment cost of investment, and satisfies ’ > 0 and ’’ > 0.7

Profit maximization gives us the unique steady state optimal policy (K*, I*, L*), which satisfies

f1 ( K *, L *)-f '( I *) = R , (3)

f 2 (K*, L*)  w, and (4)

d K*= I *. (5)

Since the production function is CRS, the labor market condition (4) determines the capital-to- labor ratio, given the real wage w. By substituting L* in (3) by the implicit function of K* using

(4), and substituting I* by (5), the capital market condition (3) becomes a function of K* only.

This determines K*, which is unique because the production function is concave while the adjustment cost function is convex. Similarly, the transition path of (K,L) to the steady state is uniquely determined.8

In a fully liberalized financial sector, each firm faces the same market interest rate, R, and can invest as much as it wants. This implies that the marginal returns to capital, given by (3), are equal across firms. In a nonliberalized financial sector, however, governments may impose price controls (e.g., interest rate floors or ceilings) or quantity controls (e.g., directed credit). As we will show below, this generates variation in the marginal returns to capital across firms.

As an example of price controls, consider the case where governments control interest rates and apply different rates to different firms. In this case, each firm will still set its marginal return to

7 Allowing for the presence of low fixed-adjustment costs would not change the results.

8 We assume that these adjustments are quick—i.e., can be completed within a year—so that the steady state values can be approximated with annual data. - 11 - capital equal to its interest rate, but firms facing higher interest rates will need to set a lower steady state level of capital, K*, implying a higher marginal return to capital. In other words, variation in interest rates across firms generates variation in returns across firms.

As an example of quantity controls, consider the case where interest rates are equal, but the investment amount I is determined by the government—either directly, via control of firms’ investment plans, or indirectly, via credit allocation. Let us denote this amount by Iˆ . In this case, firms maximize their profit function (1) subject to (2) and the additional constraint I= Iˆ .

Letting  denote the Lagrange multiplier associated with this constraint, the capital market condition (3) can then be rewritten as

** ** ) f1 ( K , L )-f '( I ) = R + l . (3’)

If firms are constrained with respect to the amount they can invest ( Iˆ < I * ),  is positive, implying that the marginal return to capital—the right hand side of equation (3’)—is higher than the market-based interest rate R. Conversely, if firms overinvest ( Iˆ > I *), as may happen, for example, when governments identify specific industries for development or employment objectives,  becomes negative. This implies that the marginal return to capital is lower than the market-based interest rate.9

The above analysis shows that, when a government controls a financial sector, either by price controls or quantity controls, this generates variation in marginal returns across firms that face

9 In the case of a uniform and binding interest rate ceiling that prevents supply from matching demand, so that banks ration credit based on noneconomic factors, the same analysis applies as for quantity controls: firms that can get credit will have low marginal returns, while those that are credit constrained will have high marginal returns. - 12 - the same production function, adjustment cost function, and real wage costs. When such controls are eliminated, credit will be reallocated from firms with low marginal returns (i.e., the overinvesting firms) to firms with high marginal returns (i.e., the underinvesting firms). We thus predict that, as financial markets become more liberalized, capital is allocated more efficiently, resulting in a more equal distribution of marginal returns to capital.10

IV. DISPERSION IN TOBIN’S Q AS A MEASURE OF ALLOCATIVE EFFICIENCY

The analysis in the previous section can be generalized to the stochastic case, where the real wage, the interest rate, and productivity are allowed to vary over time. In this case, our predictions apply to the ex ante, expected marginal returns to capital, rather than the ex post, realized marginal returns. We cannot use ex post marginal returns to test the hypothesis that financial liberalization leads to a more equal distribution of returns, because the dispersion in ex post marginal returns may actually increase after liberalization, if a better financial system leads firms to select higher-risk, higher-return projects (Obstfeld, 1994).

An imperfect but nevertheless frequently used measure of expected marginal returns to capital is

Tobin’s Q, which measures the market value of a firm’s securities relative to the replacement cost of its assets.11 More precisely, the numerator of Tobin’s Q is the sum of the market value of

10 This prediction would still follow from more complicated models with informational problems. That is, even with informational problems, the variation in marginal returns before financial liberalization would still reflect an inefficient allocation of capital, to the extent that governments allocate capital based on concerns other than its marginal productivity. 11 We initially assume, for simplicity, that the stock market valuation of firms is efficient in that it correctly reflects discounted future expected profits. However, as Morck, Yeung, and Yu (2000) suggest, the efficiency of stock market valuation itself may improve with financial liberalization. In this case, an observed decline of dispersion in Tobin’s Q could potentially be attributed solely to the improvement in stock market valuation. In order to distinguish between the effects of better valuation and better allocation, we explicitly control for stock market development (both capitalization and turnover) in Section VII, and show that the positive effect of liberalization on allocative efficiency remains. - 13 - its equity (its stock market capitalization) and the market value of its debt (its current and long- term liabilities). The denominator of Tobin’s Q is the replacement costs of all tangible assets

(property, plant, and equipment) and nontangible assets (e.g., technical expertise, trademarks, and patents). Because the total market value of equity and debt represents investors’ valuation of the firm, Tobin’s Q is essentially the discounted sum of expected future profits per asset. It should equal unity in perfectly functioning markets and in the absence of measurement errors.12

In practice, Tobin’s Q can differ from unity because of measurement error and financial constraints, among other reasons.

We construct our measure of Tobin’s Q by making four approximations that are common in the literature. First, since data on nontangible assets are not available, we follow the convention of using only tangible assets in the denominator (e.g., Blanchard, Rhee, and Summers, 1993; Bond and Cummins, 2001; and Chari and Henry, 2003). Second, since we do not have data on the market value of debt, we use the book value of debt instead.13 Third, in the absence of data on the replacement cost of tangible assets, we approximate replacement costs by adjusting book values for cumulative inflation.14 Fourth, even though marginal Q provides the best estimate of the expected marginal return to capital (Hayashi, 1982), we do not have the data necessary to

12 If the market valuation per unit of capital is higher than the replacement cost of a unit of capital, firms have an incentive to invest by issuing equity; this leads to a fall in marginal returns and hence in market valuation per unit. Similarly, if the market value of a unit of capital is lower than its replacement cost, firms have an incentive to disinvest; this leads to an increase in marginal returns, and hence an increase in market value. Only when Q equals one, firms have neither an incentive to invest nor to disinvest. Also, if there were a discrepancy between the replacement cost of assets and the market valuation for a firm, this discrepancy may be quickly arbitraged away by investment firms looking for mergers or acquisitions.

13 While there is a standard approach to convert book values of debt to market values (Blanchard, Rhee, and Summers, 1993), this cannot be applied in our case because data on corporate bond rates are not available for the relevant time period. According to Chari and Henry (2003), estimating the market value of debt would require further assumptions about unobservable corporate bond rates, which may be a cure worse than the ailment. - 14 - measure marginal Q (the ratio of the increment of market valuation to the cost of the associated investment). We therefore use the standard practice to proxy marginal Q by average Q,

We also attempt to correct for potential measurement errors and other factors that may affect the dispersion of Tobin’s Q, by adjusting our estimates of Q for industry and age effects. We adjust for industry effects in order to correct for the disparity between marginal and average Q that can arise from industry-specific production functions or adjustment cost functions.15 In addition, controlling for industry effects allows us to correct for differences in Tobin’s Q due to differences in wages across industries. We adjust for differences in the age of firms in order to correct for the fact that firms of different ages have different vintages of machines and factories.

In addition, controlling for age effects allows us to correct for the possibility that younger firms may not yet be correctly valued in the stock market.16 In order to control for industry and age effects, we run the following regression for each country and year:

J

qi=a + b� Age i � g j Industry ij e i , (6) j=1

14 Specifically, if Kt is the reported value of tangible assets in year t, the inflation-adjusted value of tangible assets is given by (Kt - (1-d )鬃 Kt-1 ) + (1- d ) K t-1 鬃 (1+ p t ) = K t + (1- d ) K t-1 (1+ p t ) , where d =0.05 is an assumed depreciation rate and p t is the inflation rate. Note that all our results continue to hold even if we do not adjust for inflation.

15 We ignore possible differences arising from patent holdings, because our focus is on developing countries, while most blueprints are produced by firms in industrial countries.

16 While it is common to also control for firm size, this is not appropriate in our case because, according to our model, the firm size distribution depends directly on the extent to which financial sectors are liberalized. - 15 -

17 where qi is the logarithm of Q for firm i=1,⋯ , I , Agei is the difference between the current

18 year and the year of establishment of firm i, and Industryij is the binary variable, taking a value of 1 if firm i belongs to industry j, and a value of 0 otherwise. Running this regression gives us a

residual, ei, that captures the component of qi that is unexplained by the fixed effects of age and industry. Using this estimate, we then construct an adjusted measure of Q for each firm, whose dispersion is free of age and industry fixed effects:

骣1 I qˆi=mean( q i ) + e i =琪 q i + e i . (7) 桫I i=1

Finally, we calculate the dispersion in qˆi by using four standard inequality measures. These four measures are (1) the Gini coefficient, (2) the mean logarithm of deviations, (3) the Theil index, and (4) the coefficient of variation. A comparison of these four indices is useful because each index has different sensitivities to different ranges of the distribution. In particular, the Gini

coefficient is most sensitive to changes in qˆi around the mean; the mean log deviation is most

sensitive to changes in qˆi at the bottom of the distribution; and the coefficient of variation is most sensitive to changes at the top end of the distribution. The Theil index is the only index that

has constant sensitivity to changes in qˆi across all ranges of the distribution. The precise definitions of the four inequality indices are given in Appendix I.

17 We use the logarithm rather than the level of Q because, given a concave production function, the distribution of log(Q) better reflects the underlying distribution of capital than the distribution of Q itself. Indeed, in our data set, the distribution of Q itself is skewed to the right, while the distribution of the logarithm of Q is close to normal.

18 In the absence of data on the year of establishment for Thai firms, we measure their age as the difference between the current year and the year in which the firm was first listed at the Thai stock exchange. - 16 -

V. DATA DESCRIPTION

One major drawback of previous studies on financial liberalization has been the lack of a comprehensive cross-country data set that captures all the facets and gradations of financial reform. Previous measures of liberalization typically referred to a onetime change in rules (e.g.,

Bekaert and Harvey, 2000), which limited investigations to episodes of liberalization. An important contribution of this paper is that it uses a newly constructed financial liberalization index, covering 36 countries over the 24-year period from 1973 to 1996.

Our financial liberalization index takes as inputs the following six policy dimensions:

 credit controls, including directed credit toward favored sectors or industries,

ceilings on credit toward other sectors, and excessively high reserve requirements;

 interest rate controls, including cases where the government directly controls

interest rates, or where floors, ceilings, or interest rate bands exist;

 entry barriers, including licensing requirements, limits to the participation of foreign

banks, and restrictions relating to bank specialization or the establishment of

universal banks;

 regulations, including operational restrictions (e.g., on staffing, branching and

advertising) which are considered repression, as well as prudential regulations, which

are considered reforms;

 state ownership in the financial sector; and - 17 -

 restrictions on international financial transactions, including restrictions on capital

and current account convertibility, and the use of multiple exchange rates.

Along each dimension, a country is given a score on a graded scale, with zero for a financial system that is fully repressed, one for partially repressed, two for largely liberalized, and three for fully liberalized.19 Policy changes, then, denote shifts in a country’s score on this scale in a given year. In some cases, such as when all state-owned banks are privatized simultaneously, or when controls on all interest rates are simultaneously abolished, policy changes will correspond to jumps of more than one unit along that dimension. Reversals, such as the imposition of capital controls or interest rate controls, are recorded as shifts from a higher to a lower score. Given its detailed construction, the database allows a much more precise determination of the magnitude and timing of various events in the financial liberalization process. A detailed description of financial policy changes for the five countries included in this study can be found in Appendix II.

Further details on the financial liberalization index can be found in Abiad and Mody (2003).

To compute Tobin’s Q, we use firm-level data from the International Finance Corporation’s

(IFC) Corporate Finance Database. This IFC database is unique in that it covers emerging markets for most of the 1980s, which is the period during which much of the financial liberalization in emerging markets took place. Although the original database contains 13 countries, we were able to use only 5 countries (India, Jordan, Korea, Malaysia, and Thailand)

19 Although the gradations are necessarily subjective, some guidelines were used to reduce the subjectivity. For example, interest rates were considered fully repressed where the government set all interest rates, partially repressed where interest rates were allowed to vary within a band or were subject to a ceiling or floor, largely liberalized if some interest rates were allowed to be completely determined by the market (or if new floating rate instruments were introduced), and fully liberalized where all interest rate restrictions had been removed. - 18 - mainly due to the limited availability of the variables needed for our study. A detailed description of the data is given in Appendix III.

VI. DESCRIPTIVE STATISTICS

In this section, we report simple descriptive statistics of financial liberalization and the dispersion in Tobin’s Q, and provide evidence for a negative unconditional correlation between the two, as predicted by our model. In the next section, we present results of more formal panel regressions and robustness checks that control for omitted variables, time trends, and endogeneity, and show that this negative relationship is highly robust.

We start by comparing the average dispersion in Tobin’s Q before and after financial liberalization, using the liberalization dates specified by Demirgüc-Kunt and Detragiache (2001), which were based solely on interest rate liberalization. The liberalization dates are 1991 for

India, 1988 for Jordan, 1991 for Korea, 1987 for Malaysia, 20 and 1989 for Thailand. This pre- and post-liberalization exercise does not make full use of the graded nature of the financial liberalization index, which we take advantage of in the panel regressions, but it is an illustrative starting point for our analysis.

Given the constraints regarding data availability and reliability, the results are supportive of a quality effect: in all cases, the dispersion in Q declined (i.e., allocative efficiency improved) following financial liberalization, although the degree of decline varied across countries. As

Figure 1 shows, efficiency increased most strongly in Jordan and India. The Gini coefficient for

Jordan, for example, dropped by 41 percent, from 0.30 to 0.17, while in India it decreased by 19

20 Interest rate decontrol in Malaysia occurred in October 1978, which predates both Demirgüc- Kunt and Detragiache’s sample and ours. However, Malaysia reimposed controls in 1985 before liberalizing them again in 1987. - 19 - percent, from 0.25 to 0.20. Interestingly, the East Asian countries in our sample showed smaller gains from financial liberalization, with the Gini coefficient decreasing by 11 percent in

Malaysia, 7 percent in Thailand, and by only 2 percent in Korea.

Comparing the relative decreases measured by the Gini coefficient and the Theil index in Figures

1 and 2, we see that the percentage decline measured by the Theil index is somewhat larger than that measured by the Gini coefficient. Because the Gini coefficient is more sensitive to changes around the center of the distribution, one interpretation of this is that most of the decrease in dispersion is coming from the tail regions. That is, the firms most affected by liberalization are those that benefited most from financial repression (low Q) and those that were most constrained by it (high Q). The mean log deviation measure, which is more sensitive to changes in the lower tail, and the measure based on the coefficient of variation, which is more sensitive to the upper tail, also show a greater decline than the Gini coefficient, confirming that both low-Q and high-Q firms were affected by financial liberalization.

The general fall in dispersion after liberalization is also apparent from Figure 2, which shows the evolution of financial liberalization and the dispersion in Q over time. Each panel contains time plots of the Gini coefficient and Theil index; the other two indices are not plotted because they are very similar to that of the Theil index. The index of financial liberalization is also plotted, with a value of zero corresponding to full financial repression and a value of one corresponding to full financial liberalization.

Another pattern that can be observed in Figure 2 is that several countries experienced a gradual increase in dispersion—a worsening of allocative efficiency—until a few years before liberalization began. Taking Jordan as an example, one observes a gradual rise in dispersion from - 20 -

1980 until 1987, when dispersion begins to fall. A similar hump-shaped pattern can be seen in

India, and to a lesser extent in Malaysia. This suggests that credit allocation worsened first, before financial liberalization was eventually implemented.21 One mechanism by which this can happen is if credit is constantly channeled to certain well-connected firms, and is denied to other firms, over a number of years. This would steadily decrease the marginal product of capital for the connected firms, as money gets poured into successively less productive investments.

Conversely, marginal productivity may remain high or even rise for the non-connected firms that are severely credit-constrained, if they are unable to maintain even existing projects.

VII. PANEL REGRESSION RESULTS

In this section, we use a more formal approach to test the significance and robustness of the effect of financial liberalization on the variation in returns across firms. First, we run panel regressions of dispersion in Q on the financial liberalization index, controlling for previously omitted variables that can also affect dispersion, including stock market liquidity, trade openness, private credit to GDP, and stock market capitalization, where the latter two variables are measures of financial deepening. The results of these regressions are reported in subsections

A and B. We then control for potential simultaneity and endogeneity by using the Arellano-Bond

(1991) dynamic panel estimator, and show in subsection C that our results generally hold up against this stringent test as well. Finally, we run several other robustness checks (including time dummies, crisis dummies, and interaction variables), which are summarized in subsection D, all of which strengthen our confidence that financial liberalization genuinely improves allocative efficiency.

21 One interesting possibility is that the gradual worsening in allocative efficiency itself spurs reform and makes liberalization more likely. - 21 -

A. Controlling for Stock Market Liquidity and Trade Openness

Although we have thus far assumed that markets are pricing stocks efficiently, it is not uncommon for stock prices to deviate from fundamentals, especially in the thin markets typically observed in developing countries and emerging markets. At any point in time, this deviation creates an additional source of dispersion in Tobin's Q across firms that does not reflect the underlying capital allocation. To distinguish this source of dispersion from the dispersion caused by improved efficiency in capital allocation, we control for stock market liquidity, measured by the ratio of stock market turnover to market capitalization, for each country and each year.

In addition to controlling for stock market liquidity, we use trade openness as a control, for two reasons. First, trade openness may act as a proxy for other reforms that may improve allocative efficiency independently of financial liberalization. For example, exports and imports are directly affected by product market reforms that involve price or quantity restrictions on goods and services. Second, countries that are more open to trade flows are generally also more open to financial flows, and a more globally integrated financial sector may improve efficiency through both better allocation and better valuation.22

Even after controlling for stock market liquidity and trade openness, we find that the effect of financial liberalization on dispersion in Q is still negative and is highly significant. As Table 1 shows, this result is robust to using different measures of dispersion, as well as to using a fixed effects or random effects model. Using a fixed-effects model, the effect of stock market liquidity is negative and generally significant, as predicted, while the effect of trade openness is negative

22 Although one dimension of the financial liberalization index measures restrictions on international financial flows, liberalization along this dimension does not automatically imply global integration; some countries have relatively small international flows despite having few restrictions on these transactions. Using actual financial flows is also unsatisfactory: greater financial integration is associated with increased flows on average, but not in any given year. - 22 - and significant at the 5 percent level. Using a random-effects model, the effect of stock market liquidity is again negative and significant, while the effect of trade openness is negative but insignificant. A Hausman test23 rejects the random-effects model in favor of the fixed-effects model, so we use fixed effects for our subsequent regressions reported below.

B. Controlling for Financial Deepening

As we discussed in our literature review, several recent studies document a positive impact from financial deepening on allocative efficiency. A relevant question to ask is, therefore, what matters more for allocative efficiency, financial liberalization or financial deepening? The answer has important policy implications, since a country can achieve financial deepening without financial liberalization, and vice versa.

In order to separate the effects of financial liberalization and financial deepening, we add to our baseline fixed-effects regression two different measures of financial deepening typically used in the literature. The first is bank credit to the private sector relative to GDP (henceforth referred to as “private credit”), an indicator of the depth of the banking sector, and the second is stock market capitalization relative to GDP, an indicator of stock market development. Data for these indicators (as well as for stock market turnover) were taken from the World Bank’s (2001)

Financial Development and Structure database.

Table 2 shows the summary statistics and correlations for the financial liberalization index and the control variables. Stock market turnover is not highly correlated with market capitalization or

23 The random-effects model requires that the error term be uncorrelated with the regressors. Under this null hypothesis, the random effects estimator is consistent and efficient, but under the alternative it is inconsistent. The fixed-effects estimator is consistent under both the null and the alternative. The Hausman test is based on a comparison of the fixed-effects and random-effects estimates. - 23 - private credit, suggesting that stock market liquidity is well measured independently of financial deepening indicators.

The results strongly suggest that financial liberalization delivers benefits that are independent of financial deepening. Indeed, the former always improves allocative efficiency, while the latter does nothing or may even be harmful. Table 3 presents these regression results, with one panel for each inequality measure. When the financial liberalization index and the two financial deepening indicators are included in separate regressions (columns 1 through 3), financial liberalization is always correctly signed (negative) and strongly significant. The valuation effect, measured by stock market turnover, is also correctly signed (negative) and significant in almost all cases. However, the two financial deepening indicators are insignificant, except in only one case, when dispersion is measured by the mean log deviation, in which case stock market capitalization is significant.

When liberalization is combined with any of the two financial deepening indicators (columns 4 through 6), financial liberalization and stock market turnover remain strongly significant and correctly signed (negative), except in one case, when the dependent variable is the mean log deviation and when stock market capitalization is controlled for. Stock market capitalization is never significant, except when dispersion is measured by mean log deviation. Surprisingly, private credit is always significant, but with the wrong sign (positive), implying that private credit expansion without financial liberalization worsens the efficiency of capital allocation. This is consistent with our view that capital allocation is distorted under financial repression. - 24 -

C. Controlling for Persistence and Endogeneity

Since both our financial liberalization index and our dispersion measures display strong persistence over time (see Figure 2), it is important to ensure that the negative correlation found between the two series does not merely reflect spurious correlation. In addition, we also need to control for endogeneity and simultaneity, to ensure that the relationship we observe between liberalization and efficiency is causal rather than simply incidental.

To control for both strong persistence and for joint endogeneity and simultaneity, we reestimate our regressions using the Arellano and Bond (1991) GMM dynamic panel estimator. We first allow for the possibility of persistence by including lagged values of the dependent variable in the regression. We then difference the regression equation to focus on the effect of changes in the regressors on changes in the dispersion of Q (eliminating the possibility of spurious regression in the case of integrated variables), and to eliminate any omitted-variable bias created by unobserved country fixed effects. Finally, we instrument the right-hand side variables (the differenced values of the original regressors) using lagged levels of the original regressors to eliminate potential parameter inconsistency arising from simultaneity bias.24

Despite the very limited sample size, our main results survive this quite stringent test. Table 4 shows that the coefficient on financial liberalization remains correctly signed in all the regressions and is statistically significant in almost all cases (again, except when using the mean log deviation as a measure of dispersion). Stock market turnover is also correctly signed in all the regressions and significant in the majority of the cases, especially when financial liberalization is included as a regressor. While stock market capitalization is never significant,

24 Specification tests based on the absence of second-order serial correlation and the Sargan test for overidentifying restrictions are used. In neither case can we reject the null hypothesis, indicating that the lagged levels are valid instruments. - 25 - private credit is always significant and positive. The effect of trade openness is again not robust among regressions.

D. Other Robustness Checks

In addition to the controls mentioned above, we conducted six more robustness checks,

against which our main results held up.

The first robustness check we conducted was to drop one country at a time, in order to

investigate whether a single country was driving the results. This could have been the

case since the sample contains only five countries, and Figures 1 and 2 seem to indicate

that effects of financial liberalization were stronger in some countries than in others. We

found, however, that our results held up in all cases.

As a second robustness check, we included a crisis dummy variable in our regressions.

Theoretically, the effect of a currency or banking crisis on allocative efficiency is

unclear. On the one hand, disruptions associated with crises, such as credit crunches and

high inflation, may impair the functioning of the financial sector and reduce allocative

efficiency. On the other hand, crises may be triggered by the gradual buildup of

imbalances, such as an overvalued exchange rate or an asset price bubble, and these

imbalances may result in a misallocation of resources. To the extent that the crisis

corrects these imbalances, it may improve allocative efficiency. The crisis dummy was

set equal to one if the country had experienced a currency crisis or banking crisis, based

on the crisis database of Bordo and others (2001). The coefficient on the crisis dummy

was found to be positive, suggesting that crises widen the variation in returns. However,

it was almost always insignificant, and it did not change any of our main results. - 26 -

Third, to find out whether the results are driven by a specific type of financial

liberalization, we ran our regressions using, in turn, only one of the six subcomponents of

the financial liberalization index, rather than the aggregate index. We found that doing so

did not change the main result, and that no single component was as robust as the

aggregate index itself. However, as the liberalization subcomponents were found to be

substantially correlated, including only one subcomponent at a time may not necessarily

help to identify which aspect of liberalization mattered most.

Fourth, we explored interactions between financial liberalization and financial deepening,

to allow for the possibility of nonlinear effects. In particular, we wanted to test whether

both financial liberalization and financial deepening are required to realize a significant

improvement in allocative efficiency. Including these interactions, however, produced no

interesting new results.

Fifth, we tried interacting the financial liberalization index with country dummies, i.e.,

allowing the coefficient on financial liberalization to vary across countries; however, this

did not generate any interesting patterns either. The financial liberalization coefficient for

India (which we used as the uninteracted coefficient) was negative but insignificant; the

coefficient for Jordan was significantly more negative than for India; the coefficients for

Korea and Malaysia were insignificantly more negative than for India; and the coefficient

for Thailand was significantly more positive than for India.25

Finally, we repeated all regressions while controlling for 3 or 5 year cumulative inflation

to eliminate any remaining measurement errors in Tobin’s Q. There are two offsetting

25 These estimations are quite fragile, as each country coefficient is estimated using only 11 or 12 observations. - 27 -

effects: on the one hand, across-the-board inflation “lifts all boats” and reduces inequality

when measured in a log scale, while, on the other hand, dispersion increases with

heterogeneous inflation variation (in location and equipments), which in turn is likely

correlated with across-the-board inflation. When we included inflation (the GDP deflator)

in the regressions, we found that the coefficient on inflation was negative and

occasionally significant, indicating that the first effect is stronger than the second.

However, all key results remained the same.

VIII. CONCLUSION

Although recent studies have found little or no effect of liberalization on the level of savings and investment, the primary finding of this paper is that liberalization does improve allocative efficiency. We thus suggest that the benefits of liberalization are realized primarily through its effect on the quality, not the quantity, of investment.

With a simple model, we predicted that financial liberalization, by equalizing access to credit, reduces the variation in expected returns across firms, which we measured by the dispersion in

Tobin’s Q. In testing this prediction, we found that financial liberalization was strongly negatively associated with the dispersion in Q and, hence, positively associated with allocative efficiency.

The positive effect of financial liberalization on allocative efficiency is surprisingly robust. As our panel regression results showed, it holds up after controlling for several possibly related omitted variables, including stock market turnover, trade openness, private credit, and stock market capitalization. It also holds up after taking into account persistence, endogeneity, and other factors. This robustness is remarkable, given the limited amount of data available and the - 28 - bias against finding a positive impact owing to the focus on large, publicly listed firms and the use of an unbalanced sample.

Three additional findings are worth noting. First, we found that financial liberalization, rather than financial deepening, mattered the most for allocative efficiency. When the liberalization index was included along with financial-deepening measures, the standard financial-deepening indicators, such as private credit and stock market capitalization, were insignificant or wrongly signed. In fact, increasing private credit typically worsened allocative efficiency, which suggests that credit growth without financial liberalization may lead to a misallocation of credit. Second, we found that stock market turnover did have an independent and positive effect on efficiency.

This reflect better valuation, since stock prices in more liquid stock markets are more accurate measures of companies’ intrinsic values. A final observation is that the dispersion in firms’ marginal returns often seems to increase prior to liberalization, indicating a steady worsening of allocative efficiency during periods of financial repression. This pattern might be explained by a long-term policy of directing credit to selected sectors or firms, while consistently denying credit to other sectors or firms. Further research would be needed to ascertain whether these conjectures are correct or whether alternative mechanisms are at work. - 29 -

Table 1. Fixed-Effects and Random-Effects Regressions

Fixed Effects Random Effects Gini Theil M.L.D. C.V.Sq. Gini Theil M.L.D. C.V.Sq. Financial Liberalization -0.114 -0.24 -0.441 -0.237 -0.011 -0.0338 -0.376 -0.0343 [2.78]*** [2.79]*** [2.11]** [2.83]*** [0.28] [0.43] [1.92]* [0.44] Stock Market Turnover -0.078 -0.162 -0.289 -0.15 -0.1062 -0.2184 -0.3452 -0.2123 [2.35]** [2.34]** [1.72]* [2.22]** [3.73]*** [3.71]*** [2.36]** [3.65]*** Trade Openness -0.393 -0.766 -0.744 -0.858 -0.0553 -0.1318 -0.169 -0.1208 [2.98]*** [2.76]*** [1.11] [3.17]*** [1.23] [1.42] [0.53] [1.32] Observations 66 66 66 66 66 66 66 66 Number of countries 5 5 5 5 5 5 5 5 R-squared 0.41 0.4 0.2 0.42 0.25 0.26 0.20 0.25 Hausman Test Statistic: 14.18 6.93 2.25 15.08 Hausman Test p -value: 0.00 0.07 0.52 0.00 Notes: Absolute value of t -statistics in brackets. M.L.D. = mean log deviation; C.V.Sq=squared coefficient of variation. The symbol * indicates significance at the 10-percent level; ** at the 5-percent level; and *** at the 1-percent level. In the Hausman Test of fixed versus random effects, the null hypothesis is that the random-effects model is valid. - 30 -

Table 2. Summary Statistics and Correlations for Control Variables

Variable Financial Liberalization Credit/GDP Market Cap Market Liquidity Trade Openness Number of Observations 75 75 75 75 75 Mean 6.3 0.6 0.4 0.5 79.9 Standard Deviation 4.2 0.2 0.5 0.4 45.3 Minimum 0.0 0.2 0.0 0.0 13.5 Maximum 13.0 1.0 2.8 1.7 184.3

Correlation with: Financial Liberalization 1.00 Credit/GDP 0.71 1.00 Market Cap 0.56 0.59 1.00 Market Liquidity 0.21 0.26 -0.07 1.00 Trade Openness 0.52 0.63 0.72 -0.29 1.00 - 31 -

Table 3. Fixed-Effects Regressions With Financial-Deepening Indicators

Dependent Variable: Gini Coefficient Financial Liberalization -0.114 -0.164 -0.137 -0.161 [2.78]*** [3.67]*** [2.88]*** [3.39]*** Private Credit 0.076 0.272 0.281 [0.69] [2.39]** [2.15]** Stock Market Capitalization -0.018 0.033 -0.005 [0.58] [0.96] [0.14] Stock Market Turnover -0.078 -0.096 -0.084 -0.105 -0.082 -0.105 [2.35]** [2.60]** [2.36]** [3.12]*** [2.47]** [3.09]*** Trade Openness -0.393 -0.490 -0.462 -0.327 -0.434 -0.319 [2.98]*** [3.64]*** [3.16]*** [2.52]** [3.13]*** [2.21]** Observations 66 66 66 66 66 66 Number of countries 5 5 5 5 5 5 R-squared 0.41 0.34 0.34 0.47 0.42 0.47

Dependent Variable: Theil Index Financial Liberalization -0.240 -0.340 -0.279 -0.329 [2.79]*** [3.61]*** [2.76]*** [3.26]*** Private Credit 0.137 0.544 0.587 [0.59] [2.26]** [2.13]** Stock Market Capitalization -0.050 0.054 -0.027 [0.76] [0.73] [0.33] Stock Market Turnover -0.162 -0.199 -0.173 -0.218 -0.170 -0.218 [2.34]** [2.55]** [2.32]** [3.05]*** [2.42]** [3.03]*** Trade Openness -0.766 -0.972 -0.890 -0.634 -0.831 -0.592 [2.76]*** [3.43]*** [2.89]*** [2.31]** [2.84]*** [1.94]* Observations 66 66 66 66 66 66 Number of countries 5 5 5 5 5 5 R-squared 0.4 0.32 0.33 0.45 0.4 0.45 - 32 -

Table 3. Fixed-Effects Regressions With Financial-Deepening Indicators (concluded)

Dependent Variable: Mean Log Deviation Financial Liberalization -0.441 -0.634 -0.298 -0.449 [2.11]** [2.74]*** [1.23] [1.90]* Private Credit 0.299 1.058 1.770 [0.54] [1.78]* [2.74]*** Stock Market Capitalization -0.311 -0.200 -0.442 [2.06]** [1.14] [2.34]** Stock Market Turnover -0.289 -0.362 -0.262 -0.396 -0.259 -0.403 [1.72]* [1.96]* [1.54] [2.26]** [1.53] [2.39]** Trade Openness -0.744 -1.117 -0.565 -0.487 -0.503 0.221 [1.11] [1.67] [0.80] [0.72] [0.71] [0.31] Observations 66 66 66 66 66 66 Number of countries 5 5 5 5 5 5 R-squared 0.20 0.15 0.20 0.25 0.22 0.31

Dependent Variable: Squared Coefficient of Variation Financial Liberalization -0.237 -0.337 -0.287 -0.334 [2.83]*** [3.68]*** [2.93]*** [3.41]*** Private Credit 0.141 0.545 0.555 [0.62] [2.32]** [2.07]** Stock Market Capitalization -0.038 0.069 -0.007 [0.58] [0.98] [0.09] Stock Market Turnover -0.150 -0.188 -0.163 -0.206 -0.161 -0.206 [2.22]** [2.46]** [2.24]** [2.96]*** [2.35]** [2.94]*** Trade Openness -0.858 -1.060 -1.002 -0.725 -0.942 -0.715 [3.17]*** [3.83]*** [3.33]*** [2.72]*** [3.32]*** [2.41]** Observations 66 66 66 66 66 66 Number of countries 5 5 5 5 5 5 R-squared 0.42 0.35 0.35 0.47 0.43 0.47 Notes: Absolute value of t -statistics in brackets. The symbol * indicates significance at the 10-percent level; ** at the 5-percent level; and *** at the 1-percent level. - 33 -



Table 4. Arellano-Bond Dynamic Panel Regressions

Dependent Variable: Gini Coefficient Financial Liberalization -0.207 -0.208 -0.171 -0.184 [2.71]*** [2.31]** [2.49]** [2.60]*** Private Credit 0.250 0.253 0.211 [1.76]* [1.74]* [1.94]* Stock Market Capitalization 0.090 0.066 0.045 [1.63] [1.19] [1.04] Stock Market Turnover -0.090 -0.069 -0.048 -0.105 -0.079 -0.095 [2.48]** [2.10]** [1.61] [2.78]*** [2.68]*** [3.13]*** Trade Openness -0.042 -0.086 -0.244 0.030 -0.126 -0.038 [0.48] [0.87] [2.12]** [0.48] [1.50] [0.44] Lagged Dependent Variable 0.554 0.517 0.524 0.500 0.523 0.488 [14.94]*** [10.07]*** [7.16]*** [9.97]*** [9.58]*** [7.17]*** Observations 56 56 56 56 56 56 Number of countries 5 5 5 5 5 5 Second-order serial correlation (p -value): 0.60 0.33 0.27 0.70 0.53 0.65 Sargan test statistic (p -value): 1.00 0.99 0.99 1.00 1.00 1.00

Dependent Variable: Theil Index Financial Liberalization -0.445 -0.448 -0.371 -0.401 [2.98]*** [2.41]** [2.90]*** [2.73]*** Private Credit 0.554 0.561 0.479 [2.08]** [1.96]* [2.20]** Stock Market Capitalization 0.188 0.136 0.087 [1.57] [1.17] [0.94] Stock Market Turnover -0.194 -0.148 -0.104 -0.226 -0.171 -0.207 [2.31]** [2.00]** [1.51] [2.56]** [2.44]** [2.75]*** Trade Openness -0.039 -0.129 -0.465 0.120 -0.211 -0.013 [0.21] [0.67] [2.05]** [0.90] [1.26] [0.07] Lagged Dependent Variable 0.493 0.452 0.465 0.433 0.463 0.422 [9.88]*** [8.13]*** [5.31]*** [6.90]*** [6.25]*** [5.01]*** Observations 56 56 56 56 56 56 Number of countries 5 5 5 5 5 5 Second-order serial correlation (p -value): 0.77 0.59 0.46 0.91 0.70 0.85 Sargan test statistic (p -value): 1.00 1.00 1.00 1.00 1.00 1.00 - 34 -

Table 4. Arellano-Bond Dynamic Panel Regressions (concluded)

Dependent Variable: Mean Log Deviation Financial Liberalization -0.542 -0.548 -0.474 -0.615 [2.30]** [1.48] [2.13]** [1.59] Private Credit 1.976 1.980 2.110 [3.51]*** [3.11]*** [3.38]*** Stock Market Capitalization 0.188 0.122 -0.119 [0.71] [0.44] [0.69] Stock Market Turnover -0.306 -0.329 -0.197 -0.423 -0.284 -0.453 [1.35] [1.64] [0.96] [1.72]* [1.36] [1.81]* Trade Openness 0.106 0.421 -0.350 0.738 -0.027 0.908 [0.14] [0.77] [0.79] [1.00] [0.05] [1.33] Lagged Dependent Variable 0.032 -0.063 0.039 -0.068 0.034 -0.076 [0.30] [0.59] [0.36] [0.54] [0.30] [0.68] Observations 56 56 56 56 56 56 Number of countries 5 5 5 5 5 5 Second-order serial correlation (p-value): 0.56 0.51 0.58 0.50 0.58 0.49 Sargan test statistic (p-value): 1.00 1.00 1.00 1.00 1.00 1.00

Dependent Variable: Squared Coefficient of Variation Financial Liberalization -0.463 -0.465 -0.394 -0.418 [3.31]*** [2.74]*** [3.07]*** [3.12]*** Private Credit 0.480 0.485 0.406 [1.81]* [1.80]* [2.08]** Stock Market Capitalization 0.182 0.127 0.085 [1.63] [1.17] [1.02] Stock Market Turnover -0.193 -0.140 -0.100 -0.220 -0.172 -0.201 [2.55]** [2.04]** [1.58] [2.84]*** [2.77]*** [3.20]*** Trade Openness -0.078 -0.198 -0.513 0.061 -0.241 -0.071 [0.50] [0.94] [1.87]* [0.52] [1.24] [0.35] Lagged Dependent Variable 0.556 0.528 0.528 0.511 0.527 0.499 [23.80]*** [10.45]*** [8.60]*** [13.04]*** [14.41]*** [9.26]*** Observations 56 56 56 56 56 56 Number of countries 5 5 5 5 5 5 Second-order serial correlation (p-value): 0.40 0.19 0.14 0.50 0.35 0.46 Sargan test statistic (p-value): 1.00 0.99 1.00 1.00 1.00 1.00 Notes: Robust z -statistics in brackets. The symbol * indicates significance at the 10-percent level; ** at the 5-percent level; and *** at the 1-percent level. - 35 -

Figure 1. Dispersion Measures, Pre- and Post-Liberalization

Gini Coefficient Before T heil Index Aft er

0.5 0.5

0.4 0.4

0 . 3 0 0.3 0 . 2 8 0.3 0 . 2 5 0 . 2 5 0 . 2 3 0 . 2 1 0 . 2 0 0 . 2 1 0 . 2 1 0.2 0 . 1 7 0.2 0 . 1 5 0 . 1 4 0 . 1 1 0 . 1 1 0 . 0 9 0.1 0.1 0 . 0 7 0 . 0 8 0 . 0 7 0 . 0 7 0 . 0 5

0.0 0.0 India Jordan Korea Malaysia T hailand India Jordan Korea Malaysia T hailand

Mean Log Deviat ions Squared Coefficient of Variation

0.5 0.5 0 . 4 7 0 . 4 3 0.4 0.4 0 . 3 4 0.3 0.3

0 . 2 1 0 . 1 9 0.2 0.2 0 . 1 5 0 . 1 3 0 . 1 4 0 . 1 3 0 . 1 2 0 . 1 1 0 . 1 0 0 . 0 9 0 . 0 9 0 . 0 8 0.1 0 . 0 7 0 . 0 7 0 . 0 7 0 . 0 7 0.1 0 . 0 5

0.0 0.0 India Jordan Korea Malaysia T hailand India Jordan Korea Malaysia T hailand

Source: Authors' calculations. - 36 -

Figure 2. Time Plots of Financial Liberalization Index (FLI) and Q Dispersion for Five Countries

India Jordan Korea

1.0 0.4 1.0 0.4 1.0 0.4

0.8 0.8 0.8 0.3 0.3 0.3

0.6 0.6 0.6 0.2 0.2 0.2 0.4 0.4 0.4

0.1 0.1 0.1 0.2 0.2 0.2

0.0 0.0 0.0 0.0 0.0 0.0 2 4 8 2 0 6 0 2 0 4 6 8 0 2 4 0 4 6 8 0 2 4 2 8 8 8 8 8 8 9 9 8 8 8 8 9 9 9 8 8 8 8 9 9 9 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Malaysia T hailand

1.0 0.4 1.0 0.4

0.8 0.8 0.3 0.3 FLI (left axis)

0.6 0.6 GINI (right axis)

0.2 0.2 T HEIL (right axis) 0.4 0.4

0.1 0.1 0.2 0.2

0.0 0.0 0.0 0.0 7 3 4 8 2 3 5 9 1 6 0 4 8 9 8 8 9 8 8 8 9 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 1 1 1 1 1 1 1 1 1 1 1 1

Source: Author's calculations. Note: The financial liberalization index (FLI) has a 0-1 scale, with 0 indicating full financial repression and 1 indicating full financial liberalization. - 37 -

Appendix I. Measuring Dispersion in Q: Four Inequality Indices

The first, and most well-known inequality index we use is the Gini coefficient, which is defined as

1 n n ˆ ˆ GINI=2 邋 qi - q j , 2n m i=1 j = 1 (A1)

where  denotes the mean of qˆi among all n firms in a given country. Note that the Gini takes differences over all pairs of firms, i and j, as opposed to differences vis-à-vis the mean of the distribution, like the standard deviation or variance would do.

The other three measures of dispersion are all derived from the following generalized entropy

(probabilistic) class of inequality indices: 26

a 1轾 1 n 骣qˆ E(a ) = 犏 琪 i -1 , (A2) 2 - a n m a 臌犏 i=1 桫 where   0.

This general formula for the parametric class of inequality indices has a number of desirable properties, including symmetry, population replication, and decomposability, as well as scale invariance.

One commonly used inequality index that can be derived from this generalized entropy class is the mean log deviation:

1 n 骣m E(0) = log 琪 . n qˆ i=1 桫 i (A3)

26 See Cowell (1995) and Deaton (1997). - 38 -

Another well-known inequality index that is a special case of the entropy class is the Theil index:

1 n qˆ 骣qˆ E(1) = i log琪 i . (A4) n i=1 m 桫m

The final inequality index we compute is half the square of the coefficient of variation:

1 n ˆ 2 E(2) = 2 ( qi - m ) . 2nm i=1 (A5) - 39 - APPENDIX II

Appendix II. Coding of Financial Reforms in the Five Countries

INDIA Credit Controls/Reserve Requirements Cash and statutory reserve requirements remain high, and more than 40 percent of lending is still directed toward "priority sectors". Rates on small loans are still provided at lower-than-market rates. 0 throughout sample.

Interest Rates Lending and deposit rates were regulated beginning in 1962 and 1964, respectively. In October 1988 the lending rate ceiling was converted to a floor, which was eliminated in October 1994. Term deposit rates were liberalized subject to a ceiling in 1992, and were completely freed during1995-97. Controls remain on small loans (25 percent of total lending), on savings/postal deposits, and nonresident foreign exchange deposits. 0 during 1973- 87, 1 during 1988-91, 2 in 1992. Entry Barriers Entry of private banks was deregulated in January 1993, resulting in the licensing of nine new domestic and twenty-two new foreign banks. Joint Indian-foreign ventures are allowed, but foreign banks can own only up to 20 percent of equity. Some restrictions on foreign bank branching remain. 2 from 1993 onward.

Regulations Narasimham Committtee recommendations on prudential norms and standards were phased in during 1993-96. The regulatory framework was strengthened signficantly in 1992. 1 from 1992, 2 from 1993 onward.

Privatization Banks were nationalized in 1969. The banking sector is still dominated by state-owned banks, which hold some 80 percent of total assets. 0 throughout sample. International Transactions Liberalization began with the easing of some restrictions on portfolio and direct investment in 1991-93. In 1993, the exchange rate system was unified. Most remaining restrictions on current account transactions were eliminated in 1994, culminating in India’s formal acceptance of the IMF’s Article VIII. Foreign exchange regulations are still significant, as are restrictions on the short-term flows. 1 in 1991, 2 from 1994 onward.

JORDAN Credit Controls/Reserve Requirements Privileges of specialized credit institutions have been gradually phased out beginning in 1996; preferential credit facilities remain for agricultural, handicrafts, and export sectors. 0 during 1973-95, 1 from 1996 onward.

Interest Rates Interest rates were liberalized in 1988. 2 from 1988 onward. Entry Barriers In 1984, a decree limiting foreign participation in banking to 49 percent was rescinded. Legislation from 1997 onward has eased the entry of a variety of financial products. 1 during 1984-96, 2 from 1997 onward.

Regulations Prudential regulations were improved in 1988; a risk-weighted capital adequacy requirement was raised to 12 percent effective June 1997. 0 during 1973-83, 1 during 1984-96, 2 from 1997 onward.

Privatization State ownership of banks is virtually absent in Jordan. 3 throughout sample. International Transactions The IMF's Article VIII was accepted February 1995. 1 during 1973-94, 2 from 1995 onward. - 40 - APPENDIX II

KOREA Credit Controls/Reserve Requirements In 1982, credit ceilings and preferential interest rates were abolished. The use of credit and management directives was reduced during 1982-88. Reserve requirements were lowered to 5.5 percent in 1981, from more than 20 percent before 1980. 1 in 1981, 2 from 1982 onward. Interest Rates In 1984, interbank call rates and corporate bond rates were freed; interest rate ranges for other instruments were introduced. Interest rate control was reintroduced during 1989-90. Interest rate liberalization was resumed in 1991, and implemented during 1991-95. 0 during 1973-83, 1 during 1984-88, 0 during 1989-1990, 2 from 1991 onward. Entry Barriers In 1981, restrictions on bank entry and banking activities were eased; nonbank financial institutions were allowed, and limited joint ventures were permitted. In 1986, bank branching for domestic financial institutions was liberalized. 1 during 1981-85, 2 from 1986 onward. Regulations Supervisory procedures were gradually reformed during 1982-88; regulatory and support measures for the stock exchange were implemented during 1989-1992. Commercial banks were required to achieve 8-percent capital adequacy ratios in 1995. 1 during 1982-1994, 2 from 1995 onward.

Privatization As of 1980, state ownership in the banking sector was about 25 percent. The government privatized five nationwide commercial banks during 1981-83. Government ownership increased to 60 percent of the banking sector during 1997-99. 1 during 1973-90, 2 from 1981 onward. International Transactions Controls on outward and inward foreign investment were gradually eased between 1985-1996. The IMF's Article VIII was accepted in October 1988. 1 during 1985-87, 2 from 1988. MALAYSIA Credit Controls/Reserve Requirements In 1976, the net lending requirement for priority sectors was reduced to 20 percent, from 50 percent in the previous year. In 1980, the scope of priority lending was reduced, and subsidized credit was eliminated gradually. 1 during 1976-79, 2 from 1980 onward. Interest Rates Interest rates were liberalized in October 1978; controls were reimposed between October 1985 and January 1987 in response to recession, then liberalized again during 1987-91. 3 during 1978-84, 1 during 1985-86, 3 from 1987 onward. Entry Barriers In 1985, finance companies were allowed to participate in interbank market; merchant banks were allowed to issue nonnegotiable CDs. 1 from 1985 onward. Regulations A uniform risk-based capital adequacy framework was adopted in 1989. Additional reforms promoted active secondary markets in government securities, and strengthen supervisory functions. 1 during 1973-88, 2 from 1989 onward. Privatization The banking sector was privatized gradually from 1989 onward, reducing state ownership in the banking sector from more than 50 percent in 1985 to 0 percent by 2000. 1 during 1989-94, 2 during 1995-99, 3 from 2000 onward. International Transactions The IMF's Article VIII has been accepted since November 1968, and capital flows were relatively free through the 1970s and 1980s.Capital controls were eased further in 1987. Controls on short-term and portfolio inflows were temporarily reimposed in 1994, but lifted in 1995; controls on capital outflows were imposed in 1998. 2 during 1973-86, 3 during 1987-93, 2 in 1994, 3 during 1995-97, 2 in 1998. - 41 - APPENDIX II

THAILAND Credit Controls/Reserve Requirements The government gradually eliminated directed credit after 1980. 2 from 1980 onward. Interest Rates Interest rate liberalization was intiated in 1980, but put on hold through the early 1980s. In 1989 interest rate liberalization was resumed; ceilings on all types of deposits were abolished in 1989-1990, and the ceiling on loan rates was removed in 1992. 1 during 1980-88, 2 during 1989-91, 3 from 1992 onward.

Entry Barriers In 1990, foreign banks were permitted to enter with approval. The scope of financial instruments for all financial institutions was widened in 1992; finance and securities companies were allowed to set up banks outside Bangkok in 1995. 1 during 1990-91, 2 during 1992-94, 3 from 1995 onward.

Regulations Supervisory powers of the central bank were strengthened in 1985. BIS capital adequacy guidelines were adopted in 1993. 1 during 1985-92, 2 from 1993 onward. Privatization The privatization of Krung Thai bank was initiated in 1991 and a further sale was made in 1993. Several bank privatizations occured in 1999. 0 during 1973-90, 1 during 1991-98, 2 from 1999 onward.

International Transactions Capital and exchange controls were liberalized during 1989-1992; the IMF's Article VIII was accepted in 1990, and the capital account was freed in 1991. 1 during 1989-90, 2 from 1991 onward. - 42 - APPENDIX III

Appendix III. IFC Corporate Finance Database

The Corporate Finance Database produced by the International Finance Corporation (IFC) contains annual information on a maximum of the 100 largest publicly traded, nonfinancial firms in 13 developing countries: Argentina, Brazil, India, Indonesia, Jordan, Korea,

Malaysia, Mexico, Pakistan, Peru, Thailand, Turkey, and Zimbabwe. The data are derived from listed company financial information as generally published in the yearbooks of stock markets, starting in 1980. Because the early years of the sample did not contain data of sufficiently high quality for several countries (e.g., Malaysia and Thailand), the sample for these countries begins after 1980. For some of the smaller countries, fewer than 100 firms were traded or met the data availability criteria, which resulted in smaller samples.27

From the original set of thirteen countries mentioned above, we were able to use five. First, we eliminated countries that experienced hyperinflationary episodes, as this introduces large errors in balance sheet data. Second, we eliminated countries with insufficient time coverage, particularly around the period of financial liberalization. Third, we dropped countries that lacked the data required to compute Tobin’s Q or our control variables. Finally, we dropped country-year observations if they contained too few firms for a dispersion measure to be calculated with sufficient confidence. This left us with five countries: India, Jordan, Korea,

Malaysia and Thailand—the same five countries used by Chari and Henry (2003) for evaluating the impact of capital market liberalization. The data coverage for each country is summarized in Table A1.28

27 The IFC’s collection criteria were (1) the availability of sufficiently high-quality data for a reasonably large sample of firms; and (2) representation of at least one country from each continent. More details on the data sources used for each country are given in Singh and Hamid (1992) and Booth and others (2001). - 43 - APPENDIX III

The fact that the IFC database only includes large, publicly listed firms creates a bias against detecting a positive effect of liberalization on allocative efficiency and, therefore, would strengthen, rather than weaken, any finding of such a positive effect. Large firms are more likely to be well connected, and less likely to be financially constrained even under financial repression. Hence, if we observe a decrease in dispersion even among these large firms, then the efficiency gains are likely to be even larger if one could measure dispersion across firms of all sizes. Another advantage of focusing on publicly listed firms is that information on the activities of these firms is easy for investors to obtain, and therefore informational problems should be minimal.

The fact that we use an unbalanced sample is another factor that biases us against finding a decrease in dispersion. If we used a balanced sample, the data would contain only firms that survived throughout the sample period; hence, the sample would likely be biased toward firms that did not face financing constraints. An unbalanced sample would have the same bias only if the distribution across new entrants (i.e., newly listed companies on the stock market) were the same as the distribution across old entrants, that is, as long as the entry barriers to the securities market remained unchanged. However, financial liberalization should be expected to lower the entry barriers for firms that used to have financing constraints, in which case the variation among new entrants will be larger, thus increasing the dispersion in Tobin’s Q in the unbalanced sample.

28 We used slightly fewer observations than mentioned in Table A1 because of the need to remove some outliers. We eliminated outliers by first taking the logarithm of Q, the distribution of which is close to normal, and then removing all observations further than three standard deviations from the mean. We confirmed that our results were robust to using different procedures for removing outliers, e.g., excluding all observations with Q>50 before calculating the standard deviations. - 44 - APPENDIX III

Table A1. International Finance Corporation (IFC) Corporate Financial Database Coverage (Number of firms per year)

Country 1980 1981 1982 1983 1984 1985 1986 1987 India 40 64 76 79 85 85 83 78 Jordan 27 24 30 32 33 33 35 33 Korea 90 89 90 87 90 89 88 89 Malaysia 85 90 93 94 96 Thailand 33 36 38 47 Total 157 177 196 283 331 336 338 343

Country 1988 1989 1990 1991 1992 1993 1994 India 63 79 84 79 75 Jordan 33 33 31 33 32 31 31 Korea 88 87 87 84 84 73 74 Malaysia 86 87 94 87 89 91 88 Thailand 59 62 62 61 60 57 58 Total 329 348 358 344 340 252 251 - 45 -

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